Abstract

This paper presents the development of Artificial Neural Network (ANN) models for the prediction of laser
machined internal micro-channels’ dimensions and production costs. In this work, a pulsed Nd:YVO4 laser
was used for machining micro-channels in polycarbonate material. Six ANN multi-layered, feed-forward,
back-propagation models are presented which were developed on three different training data sets. The
analysed data was obtained from a 33 factorial design of experiments (DoE). The controlled parameters
were laser power, P; pulse repetition frequency, PRF; and sample translation speed; U. Measured responses
were the micro-channel width and the micro-machining operating cost per metre of produced microchannel.
The responses were sufficiently predicted within the set micro-machining parameters limits. Three
carefully selected statistical criteria were used for comparing the performance of the ANN predictive
models. The comparison showed that model which had the largest amount of training data provided the
highest degree of predictability. However, in cases where only a limited amount of ANN training data was
available, then training data taken from a Face Centred Cubic (FCC) model design provided the highest
level of predictability compared with the other examined training data sets